SafeGraph Mobility EDA Markdown

Read in Script

source("Safegraph_Script.R")

NAs introduced by coercion

rm(list=ls()[! ls() %in% c("rural_ipums","ruca", "subset_glass", 
                           "poverty_ipums", "pct_chg_aug", 
                           "industry_ipums", "aug_combined", 
                           "buffer1_vec","buffer2_perim_vec", 
                           "buffer2_vec", "cf_bf1", "cf_bf2", "gfperimvec",
                           "not_zcta", "rural_ruca", 
                           '%notin%', 'pp1', 'pp2', 'aug_combined', 'com')])

Read in Spatial Plots

plot.new() 
rasterImage(pp1,0,0,1,1)

plot.new() 
rasterImage(pp2,0,0,1,1)

Initially a single 20-acre brush fire, it rapidly grew and merged with two smaller fires that expanded to 11,000 acres during the night of September 27 into September 28.

The Glass Fire was fully contained on October 20, 2020, after burning over 67,484 acres and destroying 1,555 structures, including 308 homes and 343 commercial buildings in Napa County, as well as 334 homes in Sonoma County

Data Tables (Descriptives)

Fire Category	Total ZCTA
GF	8
BF1	17
BF2	10

Using shape files from the GlassFire, 10 km buffers were used to create the first buffer (BF1), 10km surrounding BF1 creates the second buffer (BF2).

Each perimeter has unique ZCTA that is exclusive to their perimeter.

DT::datatable(com %>%
                filter(june != 'June') %>%
                mutate(new_date = as.POSIXct(strptime(date_range_start, "%Y-%m-%d"))) %>%
                mutate(week = as_date(new_date)) %>%
                group_by(city, zcta, week, fire_cat, 
                         Poverty_Dummy_75, Agr_Dummy_75,
                         Rural_Dummy_R) %>%
                summarize(sum_visit = sum(raw_visit_counts), .groups = 'drop'), 
              caption = 'Descriptive Statistics of August Mobility', 
              filter = "top",
              extensions = c("SearchPanes", "Select", "Buttons"),
              options = list(dom = "Btip",buttons = list("searchPanes")))

DT::datatable(com %>%
                filter(june == 'June') %>%
                mutate(new_date = as.POSIXct(strptime(date_range_start, "%Y-%m-%d"))) %>%
                mutate(week = as_date(new_date)) %>%
                group_by(city, zcta, week, fire_cat, 
                         Poverty_Dummy_75, Agr_Dummy_75,
                         Rural_Dummy_R)  %>%
              summarize(sum_visit = sum(raw_visit_counts), .groups = 'drop'), 
              caption = 'Descriptive Statistics of June Mobility', 
              filter = "top",
              extensions = c("SearchPanes", "Select", "Buttons"),
              options = list(dom = "Btip",buttons = list("searchPanes")))

Visualizations

Percent Change Using Fire Category and June as Baseline

pct_chg_aug %>%
  na.omit() %>%
  group_by(week,zcta,fire_cat) %>%
  summarize(med = median(pct_chg_mon_f), .groups = 'drop') %>%
  ggplot(data =., aes(x = week, y = med, group = zcta)) +
  scale_color_manual(values=c('Blue','Green', 'Red')) +
  geom_line(aes(colour=fire_cat), alpha = 0.2) +
  geom_point(aes(colour = fire_cat), alpha = 0.8) +
  ylim(-50,200) +
  scale_x_date(breaks = "1 week", limits = as.Date(c("2020-08-31", "2020-10-12"))) +# week of Oct 12
  theme(axis.text.x = element_text(angle = 0)) + 
  labs(x="Date", y="Median Percent Change (Divide by 100)") +
  theme(panel.grid.major = element_blank(), 
        panel.grid.minor = element_blank(),
        panel.background = element_blank(), 
        axis.line = element_line(colour = "black"))  +
    annotate("rect", xmin = as.Date("2020-09-21"), xmax = as.Date("2020-10-12"), 
           ymin = -50.00, ymax = 150.00 ,
             alpha = 0, color= "orange") +
  ggtitle("Percent Change Mobility by Fire Category")

When looking at mobility: + Week of June 6, 2020 to the week of June 22, 2020 is used as a baseline + Percent change is calculated by week to week in each consecutive week in June + The median of percent change is then grouped by fire category in June and is then officially the baseline for August mobility + Then the percent change of August is then used for each consecutive week in August (August 31 - October 12) + A calculation of percent change is taken by using the weekly percentage change for August in relation to the fire category median percent change.

Fire Category	Median Percent Change
GF	0.02
BF1	0.03
BF2	0.04

Percent Change of Mobility by Rural/ Fire Category and June as Baseline

Rural Category	Total ZCTA
Rural	11
Urban	24

pct_chg_aug %>%
  na.omit() %>%
  group_by(week,zcta,Rural_Dummy_R, fire_cat) %>%
  summarize(med = median(pct_chg_mon_f), .groups = 'drop') %>%
  ggplot(data =., aes(x = week, y = med, group = zcta)) +
  scale_color_manual(values=c('Blue','Green', 'Red')) +
  geom_line(aes(colour=Rural_Dummy_R), alpha = 0.2) +
  geom_point(aes(colour = Rural_Dummy_R), alpha = 0.8) +
  ylim(-50,200) +
  scale_x_date(breaks = "1 week", limits = as.Date(c("2020-08-31", "2020-10-12"))) +# week of Oct 12
  theme(axis.text.x = element_text(angle = 0)) + 
  labs(x="Date", y="Median Percent Change (Divide by 100") + 
  facet_wrap(vars(fire_cat), nrow = 3) +
  ggtitle("Percent Change Mobility by Fire Category and Rural")

pct_chg_aug %>%
  na.omit() %>%
  group_by(week,zcta, fire_cat) %>%
  group_by(Rural_Dummy_R, week, fire_cat) %>%
  summarize(med = median(pct_chg_mon_f), .groups ='drop') %>%
  ggplot(data =., aes(x = week, y = med, group = Rural_Dummy_R)) +
  scale_color_manual(values=c('Blue','Green', 'Red')) +
  geom_line(aes(colour=Rural_Dummy_R)) +
  geom_point(size = 2) +
  scale_x_date(breaks = "1 week", limits = as.Date(c("2020-08-31", "2020-10-12")))  +# week of Oct 12
  theme(axis.text.x = element_text(angle = 0)) + facet_wrap(vars(fire_cat), nrow = 3) +ylim(-50,50) +
  ggtitle("Percent Change Mobility by Fire Category and Rural (Collapsed)") +
  labs(x="Date", y="Median Percent Change (Divide by 100") 

pct_chg_aug %>%
  na.omit() %>%
  group_by(week,zcta) %>%
  group_by(Rural_Dummy_R, week, fire_cat) %>%
  filter(fire_cat == 'GF') %>%
  summarize(med = median(pct_chg_mon_f), .groups ='drop') %>%
  ggplot(data =., aes(x = week, y = med, group = Rural_Dummy_R)) +
  scale_color_manual(values=c('Blue','Green', 'Red')) +
  geom_line(aes(colour=Rural_Dummy_R)) +
  geom_point(size = 2) +
  scale_x_date(breaks = "1 week", limits = as.Date(c("2020-08-31", "2020-10-12"))) +# week of Oct 12
  theme(axis.text.x = element_text(angle = 0)) + 
  labs(x="Date", y="Median Percent Change (Divide by 100)") +
  theme(panel.grid.major = element_blank(), 
        panel.grid.minor = element_blank(),
        panel.background = element_blank(), 
        axis.line = element_line(colour = "black"))  +
    annotate("rect", xmin = as.Date("2020-09-21"), xmax = as.Date("2020-10-12"), 
           ymin = -20, ymax = 60 ,
             alpha = 0, color= "orange") +
  ggtitle("Median Raw Visits by Date (GF/Collapsed)")

Percent Change of Mobility by Agriculture/ Fire Category and June as Baseline

Agr Category	Total ZCTA
Agr<75	26
Agr>75	9

pct_chg_aug %>%
  na.omit() %>%
  group_by(week,zcta,Agr_Dummy_75, fire_cat) %>%
  summarize(med = median(pct_chg_mon_f), .groups = 'drop') %>%
  ggplot(data =., aes(x = week, y = med, group = zcta)) +
  scale_color_manual(values=c('Blue','Green', 'Red')) +
  geom_line(aes(colour=Agr_Dummy_75), alpha = 0.2) +
  geom_point(aes(colour = Agr_Dummy_75), alpha = 0.8) +
  ylim(-50,200) +
  scale_x_date(breaks = "1 week", limits = as.Date(c("2020-08-31", "2020-10-12"))) +# week of Oct 12
  theme(axis.text.x = element_text(angle = 0)) + 
  labs(x="Date", y="Median Percent Change") + facet_wrap(vars(fire_cat), nrow = 3)

# Agriculture BY Fire Cat
pct_chg_aug %>%
  na.omit() %>%
  group_by(week,zcta, fire_cat) %>%
  group_by(Agr_Dummy_75, week, fire_cat) %>%
  summarize(med = median(pct_chg_mon_f), .groups ='drop') %>%
  ggplot(data =., aes(x = week, y = med, group = Agr_Dummy_75)) +
  scale_color_manual(values=c('Blue','Green', 'Red')) +
  geom_line(aes(colour=Agr_Dummy_75)) +
  geom_point(size = 2) +
  scale_x_date(breaks = "1 week", limits = as.Date(c("2020-08-31", "2020-10-12")))  +# week of Oct 12

  theme(axis.text.x = element_text(angle = 0)) + facet_wrap(vars(fire_cat), nrow = 3) +ylim(-50,50)

pct_chg_aug %>%
  na.omit() %>%
  group_by(week,zcta) %>%
  group_by(Agr_Dummy_75, week, fire_cat) %>%
  filter(fire_cat == 'GF') %>%
  summarize(med = median(pct_chg_mon_f), .groups ='drop') %>%
  ggplot(data =., aes(x = week, y = med, group = Agr_Dummy_75)) +
  scale_color_manual(values=c('Blue','Green', 'Red')) +
  geom_line(aes(colour=Agr_Dummy_75)) +
  geom_point(size = 2) +
  scale_x_date(breaks = "1 week", limits = as.Date(c("2020-08-31", "2020-10-12"))) +# week of Oct 12
  theme(axis.text.x = element_text(angle = 0)) + 
  labs(x="Date", y="Median Percent Change") +
  theme(panel.grid.major = element_blank(), 
        panel.grid.minor = element_blank(),
        panel.background = element_blank(), 
        axis.line = element_line(colour = "black"))  +
    annotate("rect", xmin = as.Date("2020-09-21"), xmax = as.Date("2020-10-12"), 
           ymin = -20, ymax = 60 ,
             alpha = 0, color= "orange")
  ggtitle("Median Raw Visits by Date") 

Percent Change of Mobility by Poverty/ Fire Category and June as Baseline

Pov Category	Total ZCTA
Pov<75	26
Pov>75	9

pct_chg_aug %>%
  na.omit() %>%
  group_by(week,zcta,Poverty_Dummy_75, fire_cat) %>%
  summarize(med = median(pct_chg_mon_f), .groups = 'drop') %>%
  ggplot(data =., aes(x = week, y = med, group = zcta)) +
  scale_color_manual(values=c('Blue','Green', 'Red')) +
  geom_line(aes(colour=Poverty_Dummy_75), alpha = 0.2) +
  geom_point(aes(colour = Poverty_Dummy_75), alpha = 0.8) +
  ylim(-50,200) +
  scale_x_date(breaks = "1 week", limits = as.Date(c("2020-08-31", "2020-10-12"))) +# week of Oct 12
  theme(axis.text.x = element_text(angle = 0)) + 
  labs(x="Date", y="Median Percent Change") + facet_wrap(vars(fire_cat), nrow = 3)

# Poverty BY Fire Cat
pct_chg_aug %>%
  na.omit() %>%
  group_by(week,zcta, fire_cat) %>%
  group_by(Poverty_Dummy_75, week, fire_cat) %>%
  summarize(med = median(pct_chg_mon_f), .groups ='drop') %>%
  ggplot(data =., aes(x = week, y = med, group = Poverty_Dummy_75)) +
  scale_color_manual(values=c('Blue','Green', 'Red')) +
  geom_line(aes(colour=Poverty_Dummy_75)) +
  geom_point(size = 2) +
  scale_x_date(breaks = "1 week", limits = as.Date(c("2020-08-31", "2020-10-12")))  +# week of Oct 12

  theme(axis.text.x = element_text(angle = 0)) + facet_wrap(vars(fire_cat), nrow = 3) + ylim(-75,100)

pct_chg_aug %>%
  na.omit() %>%
  group_by(week,zcta) %>%
  group_by(Poverty_Dummy_75, week, fire_cat) %>%
  filter(fire_cat == 'GF') %>%
  summarize(med = median(pct_chg_mon_f), .groups ='drop') %>%
  ggplot(data =., aes(x = week, y = med, group = Poverty_Dummy_75)) +
  scale_color_manual(values=c('Blue','Green', 'Red')) +
  geom_line(aes(colour=Poverty_Dummy_75)) +
  geom_point(size = 2) +
  scale_x_date(breaks = "1 week", limits = as.Date(c("2020-08-31", "2020-10-12"))) +# week of Oct 12
  theme(axis.text.x = element_text(angle = 0)) + 
  labs(x="Date", y="Median Percent Change") +
  theme(panel.grid.major = element_blank(), 
        panel.grid.minor = element_blank(),
        panel.background = element_blank(), 
        axis.line = element_line(colour = "black"))  +
    annotate("rect", xmin = as.Date("2020-09-21"), xmax = as.Date("2020-10-12"), 
           ymin = -25, ymax = 60 ,
             alpha = 0, color= "orange")

  ggtitle("Median Raw Visits by Date")

$title
[1] "Median Raw Visits by Date"

attr(,"class")
[1] "labels"

Looking at PM25 Levels

Grouped by Rural/Urban

pct_chg_aug %>%
  na.omit() %>%
  inner_join(., subset_glass, by = c('zcta' = 'zcta', 'week' = 'date')) %>%
  select(zcta, fire_cat, week, PM25, wf_pm25_imp, bins, pct_chg_mon_z, Rural_Dummy_R) %>%
  group_by(week,zcta) %>%
  arrange(zcta) %>%
  ggplot(data = ., aes(x = week, y = PM25, 
                               color = Rural_Dummy_R)) +
  geom_point(aes(color = Rural_Dummy_R), alpha = .5) +
  scale_color_manual(values=c('Blue','Green')) +
  geom_smooth(se = F) + 
  scale_x_date(breaks = "1 week", limits = as.Date(c("2020-08-31", "2020-10-12"))) +
  labs(title="PM25 Values by Date and Category") +
  labs(x="Date", y="PM25 Values") +
  labs(color='Nation')  +
  annotate("rect", xmin = as.Date("2020-09-21"), xmax = as.Date("2020-10-12"), 
           ymin = 0, ymax = 100 ,
             alpha = 0, color= "orange")  + facet_wrap(vars(fire_cat), nrow = 3)

Grouped by Agricultural

pct_chg_aug %>%
  na.omit() %>%
  inner_join(., subset_glass, by = c('zcta' = 'zcta', 'week' = 'date')) %>%
  select(zcta, fire_cat, week, PM25, wf_pm25_imp, bins, pct_chg_mon_z, Agr_Dummy_75) %>%
  group_by(week,zcta) %>%
  arrange(zcta) %>%
  ggplot(data = ., aes(x = week, y = PM25, 
                               color = Agr_Dummy_75)) +
  geom_point(aes(color = Agr_Dummy_75), alpha = .5) +
  scale_color_manual(values=c('Blue','Green')) +
  geom_smooth(se = F) + 
  scale_x_date(breaks = "1 week", limits = as.Date(c("2020-08-31", "2020-10-12"))) +
  labs(title="PM25 Values by Date and Category") +
  labs(x="Date", y="PM25 Values") +
  labs(color='Nation')  +
  annotate("rect", xmin = as.Date("2020-09-21"), xmax = as.Date("2020-10-12"), 
           ymin = 0, ymax = 100 ,
             alpha = 0, color= "orange")  + facet_wrap(vars(fire_cat), nrow = 3)

Grouped by Poverty

pct_chg_aug %>%
  na.omit() %>%
  inner_join(., subset_glass, by = c('zcta' = 'zcta', 'week' = 'date')) %>%
  select(zcta, fire_cat, week, PM25, wf_pm25_imp, bins, pct_chg_mon_z, Poverty_Dummy_75) %>%
  group_by(week,zcta) %>%
  arrange(zcta) %>%
  ggplot(data = ., aes(x = week, y = PM25, 
                               color = Poverty_Dummy_75)) +
  geom_point(aes(color = Poverty_Dummy_75), alpha = .5) +
  scale_color_manual(values=c('Blue','Green')) +
  geom_smooth(se = F) + 
  scale_x_date(breaks = "1 week", limits = as.Date(c("2020-08-31", "2020-10-12"))) +
  labs(title="PM25 Values by Date and Category") +
  labs(x="Date", y="PM25 Values") +
  labs(color='Nation')  +
  annotate("rect", xmin = as.Date("2020-09-21"), xmax = as.Date("2020-10-12"), 
           ymin = 0, ymax = 100 ,
             alpha = 0, color= "orange")  + facet_wrap(vars(fire_cat), nrow = 3)